Clustering

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Clustering

Clustering

Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.

Course Features

  • Lectures 0
  • Quizzes 0
  • Duration 50 hours
  • Skill level All levels
  • Language English
  • Students 0
  • Certificate No
  • Assessments Self
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